Active Learning of Linear Embeddings for Gaussian Processes

Roman Garnett, Michael Osborne, Philipp Hennig
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:509-518, 2014.

Abstract

We propose an active learning method for discovering low-dimensional structure in high- dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and impor- tant, but have hitherto presented severe practical difficulties. We further introduce a novel tech- nique for approximately marginalizing GP hyper- parameters, yielding marginal predictions robust to hyperparameter misspecification. Our method offers an efficient means of performing GP re- gression, quadrature, or Bayesian optimization in high-dimensional spaces.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-garnett14a, title = {Active Learning of Linear Embeddings for {G}aussian Processes}, author = {Garnett, Roman and Osborne, Michael and Hennig, Philipp}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {509--518}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/garnett14a/garnett14a.pdf}, url = {https://proceedings.mlr.press/r12/garnett14a.html}, abstract = {We propose an active learning method for discovering low-dimensional structure in high- dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and impor- tant, but have hitherto presented severe practical difficulties. We further introduce a novel tech- nique for approximately marginalizing GP hyper- parameters, yielding marginal predictions robust to hyperparameter misspecification. Our method offers an efficient means of performing GP re- gression, quadrature, or Bayesian optimization in high-dimensional spaces.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Active Learning of Linear Embeddings for Gaussian Processes %A Roman Garnett %A Michael Osborne %A Philipp Hennig %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-garnett14a %I PMLR %P 509--518 %U https://proceedings.mlr.press/r12/garnett14a.html %V R12 %X We propose an active learning method for discovering low-dimensional structure in high- dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and impor- tant, but have hitherto presented severe practical difficulties. We further introduce a novel tech- nique for approximately marginalizing GP hyper- parameters, yielding marginal predictions robust to hyperparameter misspecification. Our method offers an efficient means of performing GP re- gression, quadrature, or Bayesian optimization in high-dimensional spaces. %Z Reissued by PMLR on 04 October 2026.
APA
Garnett, R., Osborne, M. & Hennig, P.. (2014). Active Learning of Linear Embeddings for Gaussian Processes. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:509-518 Available from https://proceedings.mlr.press/r12/garnett14a.html. Reissued by PMLR on 04 October 2026.

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